Tsam

* Fix Hypertuning class
* Set high as new default MILP solver
* Rework README

Following functionality was added:
* a hyperparameter tuning meta class which is able to identify the optimal combination of typical periods and segments for a given time series dataset

tsam release (2.0.1) includes the following new functionalities:

* Changed dependency of scikit-learn to make tsam conda-forge runnable.

In tsam’s latest release (2.0.0) the following functionalities were included:

* A new comprehensive structure that allows for free cross-combination of clustering algorithms and cluster representations, e.g. centroids or medoids.
* A novel cluster representation method that precisely replicates the original time series value distribution in the aggregated time series based on “Hoffmann, Kotzur and Stolten (2021): The Pareto-Optimal Temporal Aggregation of Energy System Models (https://arxiv.org/abs/2111.12072)”
* Maxoids as representation algorithm which represents time series by outliers only based on “Sifa and Bauckhage (2017): Online k-Maxoids clustering”
* K-medoids contiguity: An algorithm based on “Oehrlein and Hauner (2017): A cutting-plane method for adjacency-constrained spatial aggregation” that accounts for contiguity constraints to e.g. cluster only time series in neighboring regions

This tsam release (1.1.2) includes the following new functionalities
- Added first version of the k-medoid contiguity algorithm

This tsam release (1.1.1) includes
* Significantly increased test coverage
* Separation between clustering and representation, i.e. for clustering algorithms like **Ward’s hierarchical clustering algorithm** the representation by medoids or centroids can now freely be chosen.